Distributed Reinforcement Learning: Scaling Real Systems
Distributed reinforcement learning is more than just parallelizing code; it is about solving the synchronization bottleneck. Learn why naive parallelism fails in real-world policy optimization and how to implement Actor-Learner architectures with V-trace for high-performance, asynchronous training that doesn’t melt your server infrastructure. Stop waiting for rollouts and start scaling.